Technologies I Work With
Software Engineer • Embedded Systems • ML/AI
Hi, I'm Abheek Pradhan
Building high-performance applications from edge to cloud to web
Abheek Pradhan
Computer Engineering Student | Full stack and embedded systems

About Me
I am a driven and well-rounded senior at Texas State University. I have a knack for optimization and enjoy working on challenging meaningful problems regardless of the domain. My strongest languages are C, C++, Python and Java.
Outside of programming, I enjoy traveling, gardening, reading, exercising, and various outdoor board sports.
What I'm Working On
- Research: NSF-funded
- ◦Submitted - Dual stream Kalman transformer for fall detection and HAR (2-6% improvement across 3 datasets from baselines, tested and validated in real time)
- ◦AI agents for evaluating real-time performance issues for pervasive computing
- ◦Video to sensor knowledge distillation
- ◦Creating pipelines for generating time series data via diffusion
- Building:
- ◦Full stack machine learning applications
- ◦ML inference in C++
- ◦Agentic analysis systems for monitoring real-time performance in distributed fall detection and HAR applications (server/cloud and offline Android)
- Exploring: Modern C++, RL (Reinforcement Learning), Unsupervised machine learning
- Graduating: May 2026 - Texas State University
Work Experience
Software Engineering Intern
Toshiba International Corporation
Austin, TX
- •Developed and mocked STM32 FreeRTOS firmware, added and tested features for new touchscreen interface on Toshiba MVDs. Created automated testing infrastructure from scratch on x64, ARM architectures (CMake, TDD, Python, Bash, Ruby). Built Jenkins CI/CD pipeline validating 10,000+ params via unit integration, and HIL tests to cut QA time by 60%.
- •Optimized RTOS task priorities and DMA scheduling, reducing TouchGFX CPU overhead from 41% to 18%, achieving 25% total system CPU reduction, and eliminating all timing violations to correct issues revealed by my new HIL tests. (C, C++).
- •Engineered a RAG based AI agent using Azure/AI Copilot and OCR for detecting defects in CAD drawings with 94% precision.
Research Assistant
Texas State University, NSF Funded
San Marcos, TX
- •Developed cross-modal distillation pipeline (video to IMU sensors) and personally trained custom multimodal transformers for time series forecasting under Dr. Ann Ngu. Deployed to edge devices (phones, wearables) with 92% F1 score in real time.
- •Automated data engineering and validation of 15,000+ sensor / video files via LLMs, DSP, and Computer Vision algorithms.
- •Built distributed Ray Sturm pipeline with automated validation and testing on edge devices achieving 120% speedup, added efficient attention mechanisms increasing F1 +4%, tested DSP + sensor fusion algorithms to align modalities increasing F1 +5%.
- •Deployed multimodal PyTorch / TensorFlow transformer models to edge via LiteRT and ONNX using INT8 quantization + mixed-precision training, achieving 2-3x battery life w/ sub-1% accuracy loss allowing for on device inference (NPU, GPU).
- •Refactored full-stack Android Studio app (Java / Kotlin) for ONNX, Keras, Spark and MongoDB support.
- •Built agentic dataset labeling framework for labeling 10,000+ samples of motion data to train text to motion diffusion models.
Machine Learning Engineer
Texas State University
San Marcos, TX
- •Collaborated with research team funded by Texas State CA4D $ to fine-tune Vision Transformer and M3SFC FCNN models on distributed system Slurm cluster w/ Nvidia A100 GPUs. Created custom dataset achieving 98% precision for defect detection.
- •Built production backend REST API using Pothou FastAPI, PostgreSQL, and Docker for deployment on Huggingface; implemented server side async request handling and batch processing to handle concurrent React Native mobile app requests.
- •Accelerated inference via layer fusion and ONNX to TensorRT engine conversion; reducing latency and cloud costs by 40%.
- •Built supervised learning computer vision labeling pipeline leveraging Detectron2, CVAT, and vision LLMs with MLOps, implemented active learning loop for low-confidence samples, reducing manual labeling hours by 80%.
Featured Projects
A selection of my recent work showcasing my skills in full-stack development, embedded systems, and AI/ML
Textbook2Video - 2nd Place Antler X Nvidia Hackathon
TLDR: Live Demo
Agentic LangChain pipeline that turns PDFs into animated, narrated lessons using Deepseek OCR and ElevenLabs TTS; fine-tuned Llama 3 via LoRA + 4b quantization on Brev and containerized the model for live deployment on HuggingFace.
NeuroNest - ML Defect Detection System
TLDR: Live Deployment
Production-grade defect detection system using Vision Transformers and MASK R-CNN fine-tuned on distributed A100 GPUs, achieving 98% precision. Built FastAPI REST service with PostgreSQL/Docker deployed on Huggingface; accelerated inference via TensorRT reducing latency and costs by 40%. Implemented automated CV labeling pipeline with Detectron2, CVAT, and vision LLMs, cutting manual labeling hours by 80%.
FusionTransformer - Multimodal Fall Detection
Dual-stream transformer architecture for real-time fall detection on smartwatches, fusing accelerometer and gyroscope data through Kalman filtering. Implements Squeeze-and-Excitation attention and cross-modal knowledge distillation, validated on 51-subject SmartFallMM dataset with LOSO cross-validation.
FPGA-Optimized Facial Recognition
TLDR: YouTube
Facial recognition on AMD Kria KV260 SoC achieving 99.47% accuracy with ensemble detection/landmark models; engineered zero-copy DMA + hardware-accelerated GStreamer pipeline with INT8 Vitis AI/Vivado optimizations, delivering 100x CPU speedup and 30–500+ FPS throughput.
Sensor Fusion for Human Activity Recognition
Cross-modal distillation pipeline for fall detection using accelerometer/gyroscope data with Complementary, Madgwick, Mahony, and EKF filters tuned for edge deployment and real-time responsiveness.
3D Skeleton Reconstruction from Video
Implemented GAST-NET for reconstructing 3D human skeletal joints from 2D video, pairing PyTorch-based pose estimation with computer vision preprocessing for motion analysis.
Time Series Orientation Estimation
Implemented Kalman, Extended Kalman, and complementary filters for IMU orientation estimation and sensor alignment, optimized for low-latency fall detection on embedded devices.
Smartphone-Based Fall Detection
Android fall detection app leveraging on-device IMU streams with TensorFlow Lite/ONNX models, dynamic preprocessing configs, and async sensor pipelines for reliable real-time alerts on constrained hardware.
Autonomous Chess Bot
TLDR: YouTube
Architected multi-process chess system with C++ TCP server, IPC messaging, and browser automation pipeline; containerized deployment and Stockfish orchestration deliver fully autonomous online play with 100% winrate in live games.
Real-Time Audio Transcriber
Engineered full-stack audio transcription web application with Flask WebSocket backend, React.js/Next.js frontend, hardware-accelerated Whisper AI speech-to-text, and MongoDB session storage.
Education
Bachelor of Science in Computer Science and Engineering
Texas State University
San Marcos, TX
Activities and Societies
Skills
AI/ML
- LLM
- Transformers
- CNN
- Computer Vision
- Knowledge Distillation
- MLOps
- ONNX / TensorRT
- MCP
Embedded Systems
- FreeRTOS / RTOS Scheduling
- STM32 HAL / LL API
- Kernel Development
- Device Drivers
- I2C / SPI / UART
- JTAG
- DSP
- Interrupts
- Low-noise ADC / Analog Front Ends
Languages
- Python
- C
- C++
- Java
- JavaScript
- TypeScript
- C#
- VHDL
Backend & Data
- Node.js
- Express.js
- .NET
- REST API
- PostgreSQL
- MySQL
- NoSQL
- Spark
Testing & Process
- Selenium
- Agile
- SCRUM
- Jira
Networking
- Network Protocols
- TCP/IP
- REST API
Frontend & Frameworks
- React
- Next.js
- Angular
- Tailwind CSS
- Qt
- UI/UX Design
- Streamlit
Cloud & DevOps
- AWS
- Docker
- Kubernetes
- CI/CD
- Git
- Linux
- Salesforce

